What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
It addresses the problem of fragmented analysis tools for multi-objective optimization users, but is incremental as it synthesizes existing methods rather than introducing new ones.
The paper surveys decision-support methods for analyzing trade-offs in multi-objective optimization solutions, aiming to unify scattered approaches across fields and reduce entry barriers for researchers and practitioners.
We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by MOO algorithms are scattered across fields. We provide an overview of the advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and ethics. We synthesize these methods drawing from different fields of research to build a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.